sklearn.metrics.average_precision_score

sklearn.metrics.average_precision_score(y_true, y_score)

Compute average precision (AP) from prediction scores

This score corresponds to the area under the precision-recall curve.

Note: this implementation is restricted to the binary classification task.

Parameters :

y_true : array, shape = [n_samples]

True binary labels.

y_score : array, shape = [n_samples]

Target scores, can either be probability estimates of the positive class, confidence values, or binary decisions.

Returns :

average_precision : float

See also

roc_auc_score

Area under the ROC curve

precision_recall_curve

Compute precision-recall pairs for different probability thresholds

References

[R130]

Wikipedia entry for the Average precision

Examples

>>> import numpy as np
>>> from sklearn.metrics import average_precision_score
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> average_precision_score(y_true, y_scores)  
0.79...